Overview

Dataset statistics

Number of variables25
Number of observations145460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.6 MiB
Average record size in memory170.0 B

Variable types

Numeric20
Categorical5

Alerts

Rainfall has constant value "-0.29999999999999993" Constant
MinTemp is highly correlated with Temp9amHigh correlation
MaxTemp is highly correlated with Temp9am and 1 other fieldsHigh correlation
WindGustDir is highly correlated with WindDir3pmHigh correlation
WindDir3pm is highly correlated with WindGustDirHigh correlation
Humidity9am is highly correlated with Humidity3pmHigh correlation
Humidity3pm is highly correlated with Humidity9amHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Temp9am is highly correlated with MinTemp and 1 other fieldsHigh correlation
Temp3pm is highly correlated with MaxTempHigh correlation
MinTemp is highly correlated with Temp9amHigh correlation
MaxTemp is highly correlated with Temp9am and 1 other fieldsHigh correlation
WindGustDir is highly correlated with WindDir3pmHigh correlation
WindDir3pm is highly correlated with WindGustDirHigh correlation
Humidity9am is highly correlated with Humidity3pmHigh correlation
Humidity3pm is highly correlated with Humidity9amHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MaxTemp and 1 other fieldsHigh correlation
MinTemp is highly correlated with Temp9amHigh correlation
MaxTemp is highly correlated with Temp9am and 1 other fieldsHigh correlation
Humidity9am is highly correlated with Humidity3pmHigh correlation
Humidity3pm is highly correlated with Humidity9amHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Temp9am is highly correlated with MinTemp and 1 other fieldsHigh correlation
Temp3pm is highly correlated with MaxTempHigh correlation
Rainfall is highly correlated with WindSpeed9am and 3 other fieldsHigh correlation
WindSpeed9am is highly correlated with RainfallHigh correlation
RainTomorrow is highly correlated with RainfallHigh correlation
Evaporation is highly correlated with RainfallHigh correlation
RainToday is highly correlated with RainfallHigh correlation
MinTemp is highly correlated with MaxTemp and 1 other fieldsHigh correlation
MaxTemp is highly correlated with MinTemp and 2 other fieldsHigh correlation
WindGustDir is highly correlated with WindDir9am and 1 other fieldsHigh correlation
WindDir9am is highly correlated with WindGustDir and 2 other fieldsHigh correlation
WindDir3pm is highly correlated with WindGustDir and 1 other fieldsHigh correlation
WindSpeed9am is highly correlated with WindDir9amHigh correlation
Humidity9am is highly correlated with Humidity3pmHigh correlation
Humidity3pm is highly correlated with Humidity9amHigh correlation
Pressure9am is highly correlated with Pressure3pmHigh correlation
Pressure3pm is highly correlated with Pressure9amHigh correlation
Temp9am is highly correlated with MinTemp and 2 other fieldsHigh correlation
Temp3pm is highly correlated with MaxTemp and 1 other fieldsHigh correlation
Location has 3193 (2.2%) zeros Zeros
Sunshine has 4515 (3.1%) zeros Zeros
WindGustDir has 9181 (6.3%) zeros Zeros
WindDir9am has 9176 (6.3%) zeros Zeros
WindDir3pm has 8472 (5.8%) zeros Zeros
Cloud9am has 14044 (9.7%) zeros Zeros
Cloud3pm has 8421 (5.8%) zeros Zeros

Reproduction

Analysis started2022-07-22 16:09:34.212614
Analysis finished2022-07-22 16:12:20.943449
Duration2 minutes and 46.73 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Location
Real number (ℝ≥0)

ZEROS

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.79352399
Minimum0
Maximum48
Zeros3193
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-07-22T21:42:21.128212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median24
Q336
95-th percentile46
Maximum48
Range48
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.22868729
Coefficient of variation (CV)0.5980067223
Kurtosis-1.215750407
Mean23.79352399
Median Absolute Deviation (MAD)12
Skewness0.01540564132
Sum3461006
Variance202.4555421
MonotonicityNot monotonic
2022-07-22T21:42:21.396119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
93436
 
2.4%
373344
 
2.3%
133193
 
2.2%
183193
 
2.2%
73193
 
2.2%
03193
 
2.2%
313193
 
2.2%
153193
 
2.2%
13040
 
2.1%
223040
 
2.1%
Other values (39)113442
78.0%
ValueCountFrequency (%)
03193
2.2%
13040
2.1%
23040
2.1%
33040
2.1%
43009
2.1%
53040
2.1%
63040
2.1%
73193
2.2%
83040
2.1%
93436
2.4%
ValueCountFrequency (%)
483009
2.1%
473040
2.1%
463009
2.1%
453009
2.1%
443009
2.1%
433006
2.1%
423009
2.1%
411578
1.1%
403039
2.1%
393040
2.1%

MinTemp
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.955158807
Minimum-5.95
Maximum3.15
Zeros159
Zeros (%)0.1%
Negative3464
Negative (%)2.4%
Memory size1.1 MiB
2022-07-22T21:42:21.678972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-5.95
5-th percentile1.8
Q13.15
median3.15
Q33.15
95-th percentile3.15
Maximum3.15
Range9.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8773942461
Coefficient of variation (CV)0.2969025706
Kurtosis36.1463665
Mean2.955158807
Median Absolute Deviation (MAD)0
Skewness-5.669254062
Sum429857.4
Variance0.7698206631
MonotonicityNot monotonic
2022-07-22T21:42:21.930839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.15134139
92.2%
2.9366
 
0.3%
3.1353
 
0.2%
3351
 
0.2%
2.6328
 
0.2%
2.5326
 
0.2%
2.3313
 
0.2%
2.7313
 
0.2%
2.4289
 
0.2%
1.8280
 
0.2%
Other values (83)8402
 
5.8%
ValueCountFrequency (%)
-5.9571
< 0.1%
-5.97
 
< 0.1%
-5.810
 
< 0.1%
-5.76
 
< 0.1%
-5.66
 
< 0.1%
-5.512
 
< 0.1%
-5.48
 
< 0.1%
-5.315
 
< 0.1%
-5.213
 
< 0.1%
-5.113
 
< 0.1%
ValueCountFrequency (%)
3.15134139
92.2%
3.1353
 
0.2%
3351
 
0.2%
2.9366
 
0.3%
2.8266
 
0.2%
2.7313
 
0.2%
2.6328
 
0.2%
2.5326
 
0.2%
2.4289
 
0.2%
2.3313
 
0.2%

MaxTemp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct103
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.77265296
Minimum2.7
Maximum12.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:22.213707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile12.9
Q112.9
median12.9
Q312.9
95-th percentile12.9
Maximum12.9
Range10.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8331840481
Coefficient of variation (CV)0.06523187082
Kurtosis91.48722277
Mean12.77265296
Median Absolute Deviation (MAD)0
Skewness-9.03551999
Sum1857910.1
Variance0.694195658
MonotonicityNot monotonic
2022-07-22T21:42:23.205326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.9138238
95.0%
2.7360
 
0.2%
12.4331
 
0.2%
12.8323
 
0.2%
12.6319
 
0.2%
12.5292
 
0.2%
12.7280
 
0.2%
12.3259
 
0.2%
12.1252
 
0.2%
12.2247
 
0.2%
Other values (93)4559
 
3.1%
ValueCountFrequency (%)
2.7360
0.2%
2.85
 
< 0.1%
2.920
 
< 0.1%
321
 
< 0.1%
3.19
 
< 0.1%
3.212
 
< 0.1%
3.312
 
< 0.1%
3.412
 
< 0.1%
3.510
 
< 0.1%
3.69
 
< 0.1%
ValueCountFrequency (%)
12.9138238
95.0%
12.8323
 
0.2%
12.7280
 
0.2%
12.6319
 
0.2%
12.5292
 
0.2%
12.4331
 
0.2%
12.3259
 
0.2%
12.2247
 
0.2%
12.1252
 
0.2%
12214
 
0.1%

Rainfall
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
-0.29999999999999993
145460 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters2909200
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.29999999999999993
2nd row-0.29999999999999993
3rd row-0.29999999999999993
4th row-0.29999999999999993
5th row-0.29999999999999993

Common Values

ValueCountFrequency (%)
-0.29999999999999993145460
100.0%

Length

2022-07-22T21:42:23.440820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T21:42:23.681670image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.29999999999999993145460
100.0%

Most occurring characters

ValueCountFrequency (%)
92181900
75.0%
-145460
 
5.0%
0145460
 
5.0%
.145460
 
5.0%
2145460
 
5.0%
3145460
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2618280
90.0%
Dash Punctuation145460
 
5.0%
Other Punctuation145460
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
92181900
83.3%
0145460
 
5.6%
2145460
 
5.6%
3145460
 
5.6%
Dash Punctuation
ValueCountFrequency (%)
-145460
100.0%
Other Punctuation
ValueCountFrequency (%)
.145460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2909200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
92181900
75.0%
-145460
 
5.0%
0145460
 
5.0%
.145460
 
5.0%
2145460
 
5.0%
3145460
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2909200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
92181900
75.0%
-145460
 
5.0%
0145460
 
5.0%
.145460
 
5.0%
2145460
 
5.0%
3145460
 
5.0%

Evaporation
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0.1999999999999993
145018 
0.0
 
428
0.1
 
14

Length

Max length18
Median length18
Mean length17.95442046
Min length3

Characters and Unicode

Total characters2611650
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.1999999999999993
2nd row0.1999999999999993
3rd row0.1999999999999993
4th row0.1999999999999993
5th row0.1999999999999993

Common Values

ValueCountFrequency (%)
0.1999999999999993145018
99.7%
0.0428
 
0.3%
0.114
 
< 0.1%

Length

2022-07-22T21:42:23.882211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T21:42:24.102628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.1999999999999993145018
99.7%
0.0428
 
0.3%
0.114
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
92030252
77.7%
0145888
 
5.6%
.145460
 
5.6%
1145032
 
5.6%
3145018
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2466190
94.4%
Other Punctuation145460
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
92030252
82.3%
0145888
 
5.9%
1145032
 
5.9%
3145018
 
5.9%
Other Punctuation
ValueCountFrequency (%)
.145460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2611650
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
92030252
77.7%
0145888
 
5.6%
.145460
 
5.6%
1145032
 
5.6%
3145018
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2611650
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
92030252
77.7%
0145888
 
5.6%
.145460
 
5.6%
1145032
 
5.6%
3145018
 
5.6%

Sunshine
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.757248041
Minimum0
Maximum1.9
Zeros4515
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:24.291598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.9
median1.9
Q31.9
95-th percentile1.9
Maximum1.9
Range1.9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4490663585
Coefficient of variation (CV)0.2555509229
Kurtosis8.400603661
Mean1.757248041
Median Absolute Deviation (MAD)0
Skewness-3.133224841
Sum255609.3
Variance0.2016605943
MonotonicityNot monotonic
2022-07-22T21:42:24.496969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.9128527
88.4%
04515
 
3.1%
0.11045
 
0.7%
0.21008
 
0.7%
0.3833
 
0.6%
0.7664
 
0.5%
1639
 
0.4%
0.5622
 
0.4%
0.8619
 
0.4%
0.4619
 
0.4%
Other values (11)6369
 
4.4%
ValueCountFrequency (%)
04515
3.1%
0.11045
 
0.7%
0.21008
 
0.7%
0.3833
 
0.6%
0.4619
 
0.4%
0.5622
 
0.4%
0.6572
 
0.4%
0.7664
 
0.5%
0.8619
 
0.4%
0.9615
 
0.4%
ValueCountFrequency (%)
1.9128527
88.4%
1.9581
 
0.4%
1.8589
 
0.4%
1.7585
 
0.4%
1.6591
 
0.4%
1.5568
 
0.4%
1.4578
 
0.4%
1.3560
 
0.4%
1.2613
 
0.4%
1.1517
 
0.4%

WindGustDir
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.306565379
Minimum0
Maximum16
Zeros9181
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-07-22T21:42:24.733523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q313
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.971721938
Coefficient of variation (CV)0.5985292009
Kurtosis-1.206774913
Mean8.306565379
Median Absolute Deviation (MAD)4
Skewness-0.1079607714
Sum1208273
Variance24.71801903
MonotonicityNot monotonic
2022-07-22T21:42:24.922294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1610326
 
7.1%
139915
 
6.8%
99418
 
6.5%
39313
 
6.4%
109216
 
6.3%
09181
 
6.3%
89168
 
6.3%
159069
 
6.2%
128967
 
6.2%
118736
 
6.0%
Other values (7)52151
35.9%
ValueCountFrequency (%)
09181
6.3%
18104
5.6%
27372
5.1%
39313
6.4%
47133
4.9%
56548
4.5%
66620
4.6%
78122
5.6%
89168
6.3%
99418
6.5%
ValueCountFrequency (%)
1610326
7.1%
159069
6.2%
148252
5.7%
139915
6.8%
128967
6.2%
118736
6.0%
109216
6.3%
99418
6.5%
89168
6.3%
78122
5.6%

WindGustSpeed
Real number (ℝ≥0)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.17683556
Minimum8.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:25.127039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8.5
5-th percentile20
Q123.5
median23.5
Q323.5
95-th percentile23.5
Maximum23.5
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.411944224
Coefficient of variation (CV)0.06092049194
Kurtosis33.48348705
Mean23.17683556
Median Absolute Deviation (MAD)0
Skewness-5.434555698
Sum3371302.5
Variance1.993586491
MonotonicityNot monotonic
2022-07-22T21:42:25.315510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
23.5135215
93.0%
222810
 
1.9%
202627
 
1.8%
191751
 
1.2%
171387
 
1.0%
15835
 
0.6%
13532
 
0.4%
11192
 
0.1%
991
 
0.1%
8.520
 
< 0.1%
ValueCountFrequency (%)
8.520
 
< 0.1%
991
 
0.1%
11192
 
0.1%
13532
 
0.4%
15835
 
0.6%
171387
 
1.0%
191751
 
1.2%
202627
 
1.8%
222810
 
1.9%
23.5135215
93.0%
ValueCountFrequency (%)
23.5135215
93.0%
222810
 
1.9%
202627
 
1.8%
191751
 
1.2%
171387
 
1.0%
15835
 
0.6%
13532
 
0.4%
11192
 
0.1%
991
 
0.1%
8.520
 
< 0.1%

WindDir9am
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.943826482
Minimum0
Maximum16
Zeros9176
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-07-22T21:42:25.520301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median8
Q312
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.921740236
Coefficient of variation (CV)0.6195679434
Kurtosis-1.190664591
Mean7.943826482
Median Absolute Deviation (MAD)4
Skewness0.03523503421
Sum1155509
Variance24.22352695
MonotonicityNot monotonic
2022-07-22T21:42:25.725163image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
311758
 
8.1%
1610566
 
7.3%
99287
 
6.4%
09176
 
6.3%
109112
 
6.3%
78749
 
6.0%
88659
 
6.0%
138459
 
5.8%
128423
 
5.8%
58129
 
5.6%
Other values (7)53142
36.5%
ValueCountFrequency (%)
09176
6.3%
17836
5.4%
27630
5.2%
311758
8.1%
47671
5.3%
58129
5.6%
67980
5.5%
78749
6.0%
88659
6.0%
99287
6.4%
ValueCountFrequency (%)
1610566
7.3%
157024
4.8%
147414
5.1%
138459
5.8%
128423
5.8%
117587
5.2%
109112
6.3%
99287
6.4%
88659
6.0%
78749
6.0%

WindDir3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.002316788
Minimum0
Maximum16
Zeros8472
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size568.3 KiB
2022-07-22T21:42:25.930487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median8
Q312
95-th percentile15
Maximum16
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.739864816
Coefficient of variation (CV)0.5923115695
Kurtosis-1.172833096
Mean8.002316788
Median Absolute Deviation (MAD)4
Skewness-0.08180784459
Sum1164017
Variance22.46631848
MonotonicityNot monotonic
2022-07-22T21:42:26.134935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
910838
 
7.5%
1310110
 
7.0%
89926
 
6.8%
159518
 
6.5%
109399
 
6.5%
129354
 
6.4%
38890
 
6.1%
148874
 
6.1%
78610
 
5.9%
28505
 
5.8%
Other values (7)51436
35.4%
ValueCountFrequency (%)
08472
5.8%
17857
5.4%
28505
5.8%
38890
6.1%
48263
5.7%
56590
4.5%
67870
5.4%
78610
5.9%
89926
6.8%
910838
7.5%
ValueCountFrequency (%)
164228
 
2.9%
159518
6.5%
148874
6.1%
1310110
7.0%
129354
6.4%
118156
5.6%
109399
6.5%
910838
7.5%
89926
6.8%
78610
5.9%

WindSpeed9am
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1.0
136715 
0.0
 
8745

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters436380
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0136715
94.0%
0.08745
 
6.0%

Length

2022-07-22T21:42:26.354535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T21:42:26.559681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0136715
94.0%
0.08745
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0154205
35.3%
.145460
33.3%
1136715
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number290920
66.7%
Other Punctuation145460
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0154205
53.0%
1136715
47.0%
Other Punctuation
ValueCountFrequency (%)
.145460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common436380
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0154205
35.3%
.145460
33.3%
1136715
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII436380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0154205
35.3%
.145460
33.3%
1136715
31.3%

WindSpeed3pm
Real number (ℝ≥0)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.289925065
Minimum0
Maximum7.5
Zeros1112
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:26.717074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q17.5
median7.5
Q37.5
95-th percentile7.5
Maximum7.5
Range7.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9269972122
Coefficient of variation (CV)0.1271614185
Kurtosis36.33111185
Mean7.289925065
Median Absolute Deviation (MAD)0
Skewness-5.791315262
Sum1060392.5
Variance0.8593238313
MonotonicityNot monotonic
2022-07-22T21:42:26.891098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
7.5131357
90.3%
75903
 
4.1%
63805
 
2.6%
42249
 
1.5%
01112
 
0.8%
21034
 
0.7%
ValueCountFrequency (%)
01112
 
0.8%
21034
 
0.7%
42249
 
1.5%
63805
 
2.6%
75903
 
4.1%
7.5131357
90.3%
ValueCountFrequency (%)
7.5131357
90.3%
75903
 
4.1%
63805
 
2.6%
42249
 
1.5%
21034
 
0.7%
01112
 
0.8%

Humidity9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.86299326
Minimum18
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:27.111309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile34
Q144
median44
Q344
95-th percentile44
Maximum44
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.327653003
Coefficient of variation (CV)0.1009647874
Kurtosis18.56392953
Mean42.86299326
Median Absolute Deviation (MAD)0
Skewness-4.310338068
Sum6234851
Variance18.72858051
MonotonicityNot monotonic
2022-07-22T21:42:27.331587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
44131586
90.5%
181626
 
1.1%
43925
 
0.6%
42873
 
0.6%
41780
 
0.5%
40769
 
0.5%
39713
 
0.5%
38709
 
0.5%
37605
 
0.4%
36590
 
0.4%
Other values (17)6284
 
4.3%
ValueCountFrequency (%)
181626
1.1%
19185
 
0.1%
20244
 
0.2%
21272
 
0.2%
22261
 
0.2%
23301
 
0.2%
24299
 
0.2%
25318
 
0.2%
26328
 
0.2%
27377
 
0.3%
ValueCountFrequency (%)
44131586
90.5%
43925
 
0.6%
42873
 
0.6%
41780
 
0.5%
40769
 
0.5%
39713
 
0.5%
38709
 
0.5%
37605
 
0.4%
36590
 
0.4%
35575
 
0.4%

Humidity3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.30887529
Minimum0
Maximum23
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:27.553918image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q123
median23
Q323
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.632552869
Coefficient of variation (CV)0.1180047329
Kurtosis19.88364965
Mean22.30887529
Median Absolute Deviation (MAD)0
Skewness-4.373517348
Sum3245049
Variance6.93033461
MonotonicityNot monotonic
2022-07-22T21:42:27.758664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
23132258
90.9%
221165
 
0.8%
211106
 
0.8%
201045
 
0.7%
191043
 
0.7%
17940
 
0.6%
18906
 
0.6%
16879
 
0.6%
15855
 
0.6%
14776
 
0.5%
Other values (14)4487
 
3.1%
ValueCountFrequency (%)
04
 
< 0.1%
126
 
< 0.1%
235
 
< 0.1%
363
 
< 0.1%
4113
 
0.1%
5157
 
0.1%
6242
0.2%
7303
0.2%
8422
0.3%
9481
0.3%
ValueCountFrequency (%)
23132258
90.9%
221165
 
0.8%
211106
 
0.8%
201045
 
0.7%
191043
 
0.7%
18906
 
0.6%
17940
 
0.6%
16879
 
0.6%
15855
 
0.6%
14776
 
0.5%

Pressure9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct85
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009.003112
Minimum1001.05
Maximum1009.35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:28.010849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1001.05
5-th percentile1006.6
Q11009.35
median1009.35
Q31009.35
95-th percentile1009.35
Maximum1009.35
Range8.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.325097266
Coefficient of variation (CV)0.001313273716
Kurtosis20.506332
Mean1009.003112
Median Absolute Deviation (MAD)0
Skewness-4.479227861
Sum146769592.7
Variance1.755882765
MonotonicityNot monotonic
2022-07-22T21:42:28.246786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1009.35130407
89.7%
1001.051593
 
1.1%
1009.1366
 
0.3%
1009360
 
0.2%
1009.3356
 
0.2%
1009.2352
 
0.2%
1008.9350
 
0.2%
1008.6334
 
0.2%
1008.7317
 
0.2%
1008.1315
 
0.2%
Other values (75)10710
 
7.4%
ValueCountFrequency (%)
1001.051593
1.1%
1001.140
 
< 0.1%
1001.241
 
< 0.1%
1001.345
 
< 0.1%
1001.448
 
< 0.1%
1001.544
 
< 0.1%
1001.648
 
< 0.1%
1001.755
 
< 0.1%
1001.842
 
< 0.1%
1001.974
 
0.1%
ValueCountFrequency (%)
1009.35130407
89.7%
1009.3356
 
0.2%
1009.2352
 
0.2%
1009.1366
 
0.3%
1009360
 
0.2%
1008.9350
 
0.2%
1008.8308
 
0.2%
1008.7317
 
0.2%
1008.6334
 
0.2%
1008.5291
 
0.2%

Pressure3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct85
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1006.624309
Minimum998.65
Maximum1006.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:28.514827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum998.65
5-th percentile1004.4
Q11006.95
median1006.95
Q31006.95
95-th percentile1006.95
Maximum1006.95
Range8.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.26220643
Coefficient of variation (CV)0.001253900207
Kurtosis22.2572895
Mean1006.624309
Median Absolute Deviation (MAD)0
Skewness-4.627245389
Sum146423572.1
Variance1.593165071
MonotonicityNot monotonic
2022-07-22T21:42:28.766295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1006.95130484
89.7%
998.651351
 
0.9%
1006.8392
 
0.3%
1006.7392
 
0.3%
1006.9384
 
0.3%
1006.3364
 
0.3%
1006.4361
 
0.2%
1006.2361
 
0.2%
1006355
 
0.2%
1006.6350
 
0.2%
Other values (75)10666
 
7.3%
ValueCountFrequency (%)
998.651351
0.9%
998.734
 
< 0.1%
998.840
 
< 0.1%
998.951
 
< 0.1%
99956
 
< 0.1%
999.147
 
< 0.1%
999.245
 
< 0.1%
999.345
 
< 0.1%
999.447
 
< 0.1%
999.534
 
< 0.1%
ValueCountFrequency (%)
1006.95130484
89.7%
1006.9384
 
0.3%
1006.8392
 
0.3%
1006.7392
 
0.3%
1006.6350
 
0.2%
1006.5349
 
0.2%
1006.4361
 
0.2%
1006.3364
 
0.3%
1006.2361
 
0.2%
1006.1317
 
0.2%

Cloud9am
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.446555754
Minimum0
Maximum9
Zeros14044
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:28.990815image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation2.887669495
Coefficient of variation (CV)0.6494171341
Kurtosis-1.539653284
Mean4.446555754
Median Absolute Deviation (MAD)3
Skewness-0.2285027674
Sum646796
Variance8.33863511
MonotonicityNot monotonic
2022-07-22T21:42:29.161353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
732438
22.3%
125477
17.5%
823862
16.4%
014044
9.7%
613283
9.1%
210602
 
7.3%
39583
 
6.6%
59007
 
6.2%
47161
 
4.9%
93
 
< 0.1%
ValueCountFrequency (%)
014044
9.7%
125477
17.5%
210602
 
7.3%
39583
 
6.6%
47161
 
4.9%
59007
 
6.2%
613283
9.1%
732438
22.3%
823862
16.4%
93
 
< 0.1%
ValueCountFrequency (%)
93
 
< 0.1%
823862
16.4%
732438
22.3%
613283
9.1%
59007
 
6.2%
47161
 
4.9%
39583
 
6.6%
210602
 
7.3%
125477
17.5%
014044
9.7%

Cloud3pm
Real number (ℝ≥0)

ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.508112196
Minimum0
Maximum9
Zeros8421
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:29.350487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.719516652
Coefficient of variation (CV)0.6032495496
Kurtosis-1.455817564
Mean4.508112196
Median Absolute Deviation (MAD)2
Skewness-0.225311987
Sum655750
Variance7.395770819
MonotonicityNot monotonic
2022-07-22T21:42:29.523633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
730723
21.1%
125253
17.4%
821364
14.7%
615192
10.4%
212232
 
8.4%
311724
 
8.1%
511522
 
7.9%
49027
 
6.2%
08421
 
5.8%
92
 
< 0.1%
ValueCountFrequency (%)
08421
 
5.8%
125253
17.4%
212232
 
8.4%
311724
 
8.1%
49027
 
6.2%
511522
 
7.9%
615192
10.4%
730723
21.1%
821364
14.7%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
821364
14.7%
730723
21.1%
615192
10.4%
511522
 
7.9%
49027
 
6.2%
311724
 
8.1%
212232
 
8.4%
125253
17.4%
08421
 
5.8%

Temp9am
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct94
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.529746322
Minimum-1.5
Maximum7.7
Zeros36
Zeros (%)< 0.1%
Negative443
Negative (%)0.3%
Memory size1.1 MiB
2022-07-22T21:42:29.775330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1.5
5-th percentile7
Q17.7
median7.7
Q37.7
95-th percentile7.7
Maximum7.7
Range9.2
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8749039589
Coefficient of variation (CV)0.1161930192
Kurtosis48.77634091
Mean7.529746322
Median Absolute Deviation (MAD)0
Skewness-6.592583459
Sum1095276.9
Variance0.7654569373
MonotonicityNot monotonic
2022-07-22T21:42:30.026856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.7135897
93.4%
7.7336
 
0.2%
7.6314
 
0.2%
7.2309
 
0.2%
7.4301
 
0.2%
7283
 
0.2%
7.5280
 
0.2%
7.3272
 
0.2%
7.1270
 
0.2%
6.6249
 
0.2%
Other values (84)6949
 
4.8%
ValueCountFrequency (%)
-1.5203
0.1%
-1.49
 
< 0.1%
-1.315
 
< 0.1%
-1.211
 
< 0.1%
-1.116
 
< 0.1%
-110
 
< 0.1%
-0.913
 
< 0.1%
-0.820
 
< 0.1%
-0.717
 
< 0.1%
-0.614
 
< 0.1%
ValueCountFrequency (%)
7.7135897
93.4%
7.7336
 
0.2%
7.6314
 
0.2%
7.5280
 
0.2%
7.4301
 
0.2%
7.3272
 
0.2%
7.2309
 
0.2%
7.1270
 
0.2%
7283
 
0.2%
6.9231
 
0.2%

Temp3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct97
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.80762959
Minimum2.45
Maximum11.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:30.293871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.45
5-th percentile11.6
Q111.95
median11.95
Q311.95
95-th percentile11.95
Maximum11.95
Range9.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8430120279
Coefficient of variation (CV)0.07139553469
Kurtosis74.88389983
Mean11.80762959
Median Absolute Deviation (MAD)0
Skewness-8.144270863
Sum1717537.8
Variance0.7106692792
MonotonicityNot monotonic
2022-07-22T21:42:30.576934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.95136984
94.2%
2.45452
 
0.3%
11.9383
 
0.3%
11.8359
 
0.2%
11.7329
 
0.2%
11.6317
 
0.2%
11.5301
 
0.2%
11.4299
 
0.2%
11.2280
 
0.2%
11.1268
 
0.2%
Other values (87)5488
 
3.8%
ValueCountFrequency (%)
2.45452
0.3%
2.515
 
< 0.1%
2.616
 
< 0.1%
2.710
 
< 0.1%
2.818
 
< 0.1%
2.914
 
< 0.1%
315
 
< 0.1%
3.18
 
< 0.1%
3.213
 
< 0.1%
3.36
 
< 0.1%
ValueCountFrequency (%)
11.95136984
94.2%
11.9383
 
0.3%
11.8359
 
0.2%
11.7329
 
0.2%
11.6317
 
0.2%
11.5301
 
0.2%
11.4299
 
0.2%
11.3262
 
0.2%
11.2280
 
0.2%
11.1268
 
0.2%

RainToday
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
113580 
1
31880 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0113580
78.1%
131880
 
21.9%

Length

2022-07-22T21:42:30.813173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T21:42:31.033864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0113580
78.1%
131880
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0113580
78.1%
131880
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0113580
78.1%
131880
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0113580
78.1%
131880
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113580
78.1%
131880
 
21.9%

RainTomorrow
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
113583 
1
31877 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145460
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0113583
78.1%
131877
 
21.9%

Length

2022-07-22T21:42:31.207260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-22T21:42:31.427032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0113583
78.1%
131877
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0113583
78.1%
131877
 
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number145460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0113583
78.1%
131877
 
21.9%

Most occurring scripts

ValueCountFrequency (%)
Common145460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0113583
78.1%
131877
 
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII145460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0113583
78.1%
131877
 
21.9%

date_day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.71225767
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:31.614986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.794788689
Coefficient of variation (CV)0.5597406099
Kurtosis-1.191996506
Mean15.71225767
Median Absolute Deviation (MAD)8
Skewness0.009040082176
Sum2285505
Variance77.34830808
MonotonicityNot monotonic
2022-07-22T21:42:31.835453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
14786
 
3.3%
134786
 
3.3%
234786
 
3.3%
224786
 
3.3%
214786
 
3.3%
204786
 
3.3%
194786
 
3.3%
184786
 
3.3%
174786
 
3.3%
24786
 
3.3%
Other values (21)97600
67.1%
ValueCountFrequency (%)
14786
3.3%
24786
3.3%
34786
3.3%
44786
3.3%
54786
3.3%
64786
3.3%
74786
3.3%
84786
3.3%
94786
3.3%
104786
3.3%
ValueCountFrequency (%)
312807
1.9%
304351
3.0%
294449
3.1%
284735
3.3%
274735
3.3%
264736
3.3%
254784
3.3%
244785
3.3%
234786
3.3%
224786
3.3%

date_month
Real number (ℝ≥0)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.399615014
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:32.056245image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.427261608
Coefficient of variation (CV)0.5355418412
Kurtosis-1.191879728
Mean6.399615014
Median Absolute Deviation (MAD)3
Skewness0.03034286711
Sum930888
Variance11.74612213
MonotonicityNot monotonic
2022-07-22T21:42:32.229773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
313361
9.2%
513353
9.2%
113236
9.1%
612684
8.7%
812028
8.3%
1012028
8.3%
712025
8.3%
1111669
8.0%
911640
8.0%
411550
7.9%
Other values (2)21886
15.0%
ValueCountFrequency (%)
113236
9.1%
210793
7.4%
313361
9.2%
411550
7.9%
513353
9.2%
612684
8.7%
712025
8.3%
812028
8.3%
911640
8.0%
1012028
8.3%
ValueCountFrequency (%)
1211093
7.6%
1111669
8.0%
1012028
8.3%
911640
8.0%
812028
8.3%
712025
8.3%
612684
8.7%
513353
9.2%
411550
7.9%
313361
9.2%

date_year
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.769751
Minimum2007
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2022-07-22T21:42:32.434018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2009
Q12011
median2013
Q32015
95-th percentile2017
Maximum2017
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.537683738
Coefficient of variation (CV)0.00126079187
Kurtosis-1.180677648
Mean2012.769751
Median Absolute Deviation (MAD)2
Skewness-0.04935666893
Sum292777488
Variance6.439838752
MonotonicityNot monotonic
2022-07-22T21:42:32.623084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
201617934
12.3%
201417885
12.3%
201517885
12.3%
200916789
11.5%
201016782
11.5%
201316415
11.3%
201215409
10.6%
201115407
10.6%
20178623
5.9%
20082270
 
1.6%
ValueCountFrequency (%)
200761
 
< 0.1%
20082270
 
1.6%
200916789
11.5%
201016782
11.5%
201115407
10.6%
201215409
10.6%
201316415
11.3%
201417885
12.3%
201517885
12.3%
201617934
12.3%
ValueCountFrequency (%)
20178623
5.9%
201617934
12.3%
201517885
12.3%
201417885
12.3%
201316415
11.3%
201215409
10.6%
201115407
10.6%
201016782
11.5%
200916789
11.5%
20082270
 
1.6%

Interactions

2022-07-22T21:42:11.209276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:04.087054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:10.753513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:16.974842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:23.462588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:30.116704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:37.358373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:44.160508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:51.110289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:58.774347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:05.423687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:11.841106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:18.533794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:24.792825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:31.109065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:37.985803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:44.691143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:51.608930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:58.139594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:04.756496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:11.522775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:04.438616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:11.062343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:17.275364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:23.808760image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:30.471709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:37.696764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:44.510683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:51.441723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:59.095018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:05.728393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:12.141289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:18.847147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:25.111471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:31.440101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:38.327094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:45.002742image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:51.926473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:58.456710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:05.071999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:11.842000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:04.758720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:11.359274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:17.579450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:24.126587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:30.788966image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:38.030547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:44.844683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:51.797332image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:59.412399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:06.042384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:12.459091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:19.147563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:25.406291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:31.771108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:38.669891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:45.295899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:52.220986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:58.775375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:05.374060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:12.159507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:05.074677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:11.663421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:17.865706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:24.445891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:31.129047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:38.356836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:45.195714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:52.218458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:59.727314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:06.355832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:12.756897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:19.448686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:25.706638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:32.091649image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:39.003166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:45.593316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:52.523203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:59.101117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:05.689708image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:12.508220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:05.399497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:12.011321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:18.191704image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:24.797430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:31.489161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:38.712956image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:45.542072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:52.679094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:00.087818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:06.695806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:13.096308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:19.793262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:26.038892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:32.449253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:39.348999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:45.939403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:52.854048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:59.458001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:06.022743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:12.842008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:05.747120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:12.341344image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:18.515761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:25.144740image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:31.868094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:39.063484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:45.910466image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:53.121126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:00.426224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:07.027845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:13.439121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:20.110013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:26.360752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:32.788952image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:39.708500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:46.277962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:53.186892image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-07-22T21:42:09.625323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:16.485264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:09.496181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:15.749088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:22.201381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:28.801313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:35.687114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:42.782283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:49.740099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:57.426926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:04.045931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:10.575215image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:16.908944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:23.574484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:29.820842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:36.657837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:43.362826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:50.375159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:56.863441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:03.421645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:09.925285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:16.805155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:09.812146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:16.058544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:22.500150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:29.124074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:36.043267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:43.121738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:50.079738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:57.758356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:04.373024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:10.892353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:17.209823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:23.873136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:30.132725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:36.978884image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:43.702981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:50.677069image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:57.190632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:03.742895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:10.254579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:17.135771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:10.133376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:16.375779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:22.824737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:29.463980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:36.685422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:43.461185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:50.427129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:58.128779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:04.722477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:11.205572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:17.525917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:24.178443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:30.440387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:37.305522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:44.063053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:50.993419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:57.508482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:04.074566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:10.569789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:17.453847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:10.440495image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:16.674527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:23.134447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:29.783212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:37.035022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:43.825563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:50.759296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:40:58.459938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:05.095044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:11.522180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:17.842575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:24.491335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:30.768323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:37.654139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:44.390600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:51.310308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:41:57.810686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:04.425786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-22T21:42:10.901948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-22T21:42:32.890579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-22T21:42:33.456557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-22T21:42:34.007445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-22T21:42:34.510762image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-22T21:42:34.808700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-22T21:42:17.889291image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-22T21:42:19.672083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowdate_daydate_monthdate_year
023.1512.9-0.30.21.91323.513141.07.544.022.01007.701006.958.00.07.711.95001122008
123.1512.9-0.30.21.91423.56151.07.544.023.01009.351006.957.01.07.711.95002122008
223.1512.9-0.30.21.91523.513151.07.538.023.01007.601006.958.02.07.711.95003122008
323.1512.9-0.30.21.9423.5901.07.544.016.01009.351006.950.05.07.711.95004122008
423.1512.9-0.30.21.91323.5171.07.544.023.01009.351006.007.08.07.711.95005122008
523.1512.9-0.30.21.91423.513131.07.544.023.01009.201005.408.05.07.711.95006122008
623.1512.9-0.30.21.91323.512131.07.544.019.01009.351006.951.08.07.711.95007122008
723.1512.9-0.30.21.91323.510131.07.544.019.01009.351006.958.07.07.711.95008122008
823.1512.9-0.30.21.9623.5971.07.542.09.01008.901003.601.08.07.711.95019122008
923.1512.9-0.30.21.91323.58101.07.544.023.01007.001005.701.03.07.711.951010122008

Last rows

LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pmWindSpeed9amWindSpeed3pmHumidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrowdate_daydate_monthdate_year
145450413.1512.9-0.30.21.9023.5901.07.544.023.01009.351006.958.03.07.711.95001662017
145451413.1512.9-0.30.21.9223.5821.07.544.023.01009.351006.951.08.07.711.95001762017
145452413.1512.9-0.30.21.9223.5901.07.544.023.01009.351006.956.07.07.711.95001862017
145453413.1512.9-0.30.21.9023.5201.07.544.023.01009.351006.950.06.07.711.95001962017
145454413.1512.9-0.30.21.9023.5201.07.544.023.01009.351006.958.08.07.711.95002062017
145455412.8012.9-0.30.21.9023.5911.07.544.023.01009.351006.951.01.07.711.95002162017
145456413.1512.9-0.30.21.9622.0931.07.544.021.01009.351006.958.05.07.711.95002262017
145457413.1512.9-0.30.21.9323.59141.07.544.023.01009.351006.958.01.07.711.95002362017
145458413.1512.9-0.30.21.9923.51031.07.044.023.01009.351006.953.02.07.711.95002462017
145459413.1512.9-0.30.20.71623.5221.07.544.023.01009.351006.958.08.07.711.95002562017